harborrag

harborrag is the public SDK facade and installation bundle for HarborRAG, a modular, provider-agnostic RAG framework for engineering knowledge. Importing it does not initialize provider clients or perform network I/O.

• Documentation: https://cbtw-apac.github.io/HarborRAG/
• Source: https://github.com/cbtw-apac/HarborRAG

Install

pip install "harborrag[all]"          # everything
pip install "harborrag[local]"        # local end-to-end ingestion and retrieval
pip install "harborrag[cli,qdrant]"   # or just what you need

A bare pip install harborrag installs the framework - contracts, engine, memory, adapters, and runtime - but deliberately no third-party provider clients. There is no vector store, no graph store, and no model client until you add an extra:

Conversation memory (harborrag-memory) is already a required dependency, so it is available in every install. The harborrag[memory] extra exists for explicitness and adds nothing new.

See Installation for PDF/OCR backends, editable installs, and platform notes.

Ingest and retrieve

HarborRAG exposes four async service facades - ingestion, retrieval, graph, and chat - behind one async context manager. Connectors are declared by name in your connector catalog, so credentials stay as environment references:

import asyncio

from harborrag import AccessContext, HarborRAG, IngestionRequest, RetrievalRequest


async def main() -> None:
    access = AccessContext(principal_id="user-1", tenant_id="tenant-1")

    async with HarborRAG.from_config("config/harborrag.example.yaml") as harbor:
        await harbor.ingestion.run(
            IngestionRequest(access=access, connector_name="harborrag-workspace")
        )
        results = await harbor.retrieval.search(
            RetrievalRequest(access=access, query="deployment requirements")
        )
        print(results)


asyncio.run(main())

config/harborrag.example.yaml ships in the repository checkout. Copy it next to your application and point from_config at your own path.

Chat

Chat needs a model provider, so install harborrag[chat] (or any extra that includes it, such as local, server, or all) and configure credentials first:

from harborrag import ChatPrompt, HarborChatMessage, HarborChatRequest, HarborRAG

async with HarborRAG.from_config("config/harborrag.example.yaml") as harbor:
    response = await harbor.chat.complete(
        HarborChatRequest(messages=(HarborChatMessage.user("Summarize the results"),)),
        prompt=ChatPrompt.CONCISE,
    )

Direct versus durable execution

Direct execution is the default: ingestion.run does the work inline.

The durable controls - submit, status, pause, resume, and cancel - need execution_mode: temporal in your configuration and the harborrag[temporal] extra. Calling them without both raises ExecutionCapabilityError:

task = await harbor.ingestion.submit(request)
status = await harbor.ingestion.status(task.task_id)
await harbor.ingestion.pause(task.task_id)
await harbor.ingestion.resume(task.task_id)
await harbor.ingestion.cancel(task.task_id)

Related packages

harborrag re-exports stable APIs from the workspace packages. Install one directly for a narrower dependency tree:

Development

Tests for this package live in packages/harborrag/tests/. Run them from the repository root:

uv run pytest packages/harborrag/tests

Licensed under the Apache License 2.0.